From insight to action: why prescriptive analytics is the next big step for big data
We began with descriptive, and then predictive analytics in the enterprise- and now we are approaching the era of prescriptive analytics, turning the ‘hows’ and the ‘whys’ into ‘what nows’.
Now that the era of big data is in full flow, any organisation that wants to succeed in today’s hyper-competitive market must now be implementing data-driven decision-making. Many organisations have started this journey with ‘descriptive’ analytics – the use of data mining techniques to find out what has happened, and root-cause analysis to get to the crux of why something happened.
Once they start to get comfortable with this kind of historically-based data-driven decision making, organisations can start to grow in their analytics maturity, embracing predictive analytic techniques and asking more forward-looking questions, such as ‘what is my sales forecast?’ or ‘how can I expect this trend in pricing to grow over the next three months?’
As Luis Bajuk-Yorgan, senior director of product at TIBCO Analytics explains, ‘prescriptive’ analytics is the next stage of analytics maturity in which you begin making decisions based not only on individual predictions, ‘but on an aggregated view of predicted relationships with known constraints, which yield recommendations on the best possible decision to make, given known, realistic limitations (such as cost, not overwhelming your customers with multiple contacts, organisational bandwidth, etc.).’
This decision-based analytics gives businesses the ability to best take advantage of a future opportunity. For example, questions like ‘what marketing offering will my customers best respond to, while maximising my profit?’, ‘what price point will yield the best combination of sales and profitability?’ and ‘what number of distribution centres will minimise delivery time while maximising profitability?’
‘It’s of no surprise then that prescriptive analytics is continuing to rise up the Gartner Hype Cycle as many early adopters are realising the competitive advantages that can be gained from this analysis,’ says Bajuk-Yorgan. ‘Armed with prescriptive analytics, businesses are able to improve their confidence in business outcomes.’
Though business optimisation in itself is many decades old, it’s only now that businesses have been able to reach the next logical point. In 2013 Gartner called prescriptive analytics ‘the final frontier for big data, where companies can finally turn the unprecedented levels of data in the enterprise into powerful action.
As Gartner analyst Lisa Kart explains, prescriptive analytics in itself is not the end goal, however, rather than a broadening of the set of tools available to businesses.
‘We do advice organisations doing descriptive analytics, business intelligence (BI) and reporting to increase their skills, and then move to predictive and prescriptive – but you’re not dropping the others,’ says Kart.
‘Prescriptive analytics is about applying logic and mathematics to data, with the goal to specify a prefered course of action- unlike other type of analytics the output is a decision,’ she continues. ‘Thats where it’s really different. It’s about trying to find the best decision, where best is defined by you, whether that’s lowest cost, most efficient process, higher revenue, or one that meets customer needs. Fundamentally the focus begins with the business decision.